code: def _espnet_extract_embd(self, audio):
      if len(audio.shape) == 2:
          logging.info(
              "Not support multi-channel input for ESPnet pre-trained model"
              f"Input data has shape {audio.shape}, default set avg across  channel"
          )
          audio = np.mean(audio, axis=0)
      elif len(audio.shape) > 1:
          raise ValueError(
              f"Input data has shape {audio.shape} thatis not support"
          )
      audio = torch.from_numpy(audio.astype(np.float32)).to(self.device)
      output = self.speech2embedding(audio)
      return output.cpu().numpy()
  
file_path: espnet/egs2/indic_speech/tts1/pyscripts/utils/extract_spk_embed.py
imports: import argparse
  import logging
  import os
  import sys
  from pathlib import Path
  import kaldiio
  import librosa
  import numpy
  import torch
  from tqdm.contrib import tqdm
  from espnet2.fileio.sound_scp import SoundScpReader
label_desc: Use image node_id 3 for calling the SpkEmbedExtractor obj's underlying member method code with example usage: obj._espnet_extract_embd(audio) and returns: output